sustainability

Article An Improved Evaluation Scheme for Performing Quality Assessments of Unconsolidated Cultivated Land

Lina Peng 1,2, Yan Hu 2,3, Jiyun Li 4 and Qingyun Du 1,5,6,*

1 School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; [email protected] 2 Wuhan Hongfang Real Estate & Land Appraisal. Co, Ltd., Room 508, District C, International Headquarters, Han Street, Wuchang District, Wuhan 430061, China; [email protected] 3 School of Logistics and Engineering Management, Hubei University of Economics, 8 Yangqiao Lake Road, Canglongdao Development Zone, Jiangxia District, Wuhan 430205, China 4 School of Urban and Environmental Sciences, Central China Normal University, 152 Luoyu Road, Wuhan 430079, China; [email protected] 5 Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China 6 Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geo-information, Wuhan University, 129 Luoyu Road, Wuhan 430079,China * Correspondence: [email protected]; Tel.: +86-27-6877-8842; Fax: +86-27-6877-8893

Received: 5 June 2017; Accepted: 21 July 2017; Published: 27 July 2017

Abstract: Socioeconomic factors are extrinsic factors that drive spatial variability. They play an important role in land resource systems and sometimes are more important than that of the natural setting. The study aims to build a comprehensive framework for assessing unconsolidated cultivated land (UCL) in the south-central and southwestern portions of Hubei Province, China, which have not experienced project management and land consolidation, to identify the roles of natural and especially socioeconomic factors. Moreover, the study attempts to identify the attributes and indicators that describe the characteristics of the extrinsic factors affecting land spatial variability. Assessment supplement 12 proposed land use indicators on the basis of natural factors using the method of gradation of agricultural land quality (GALQ). The overall level of cultivated land quality (CLQ) in the two study areas is moderate, and this quantity is significantly correlated with topography. Excellent and high-quality UCL are mainly distributed in the south-central plain division of Hubei Province (SCPDHP), whereas lower grades are mainly distributed in the area of the southwestern mountainous division of Hubei Province (SWMDHP). These results suggest that the pattern of small-scale agricultural development depends strongly on the labor force and is the key land use-related factor that limits the improvement of regional CLQ. Such assessments and their findings are essential for the protection of cultivated land and the adjustment of agricultural structure to promote the sustainable use of UCL.

Keywords: spatial variability; unconsolidated cultivated land (UCL); comprehensive assessment of cultivated land quality; land use and production conditions (LUPCs); sustainable land management

1. Introduction Declines in land quality caused by inappropriate land use and management practices are concerned to be an environmental and economic point [1–3]. The cultivated land system is a synthesis of natural and socioeconomic factors [4]. The interactions among these factors have an effect on land quality. Natural factors are usually intrinsic, substantial and relatively stable, whereas socioeconomic

Sustainability 2017, 9, 1312; doi:10.3390/su9081312 www.mdpi.com/journal/sustainability Sustainability 2017, 9, 1312 2 of 21 factors are extrinsic, invisible and dynamic [1]. Spatial variability in landscapes arises from a combination of intrinsic and extrinsic factors, while temporal variability is mainly up to changes in soil characteristics and rainfall patterns overtime [5]. Intrinsic spatial variability refers to natural variations in soil characteristics. These variations are related to soil formation processes, such as weathering, erosion and deposition [6]. Meanwhile, extrinsic spatial variability refers to variations caused by a lack of uniformity in management practices, e.g., tillage, chemical application and irrigation [6,7]. Exploitation of natural resources, including land, leads to degradation and is also stimulated by socioeconomic and political issues. Examples of such issues include land tenure and lack of capital [8,9]. To date, considerable research has been conducted on the intrinsic spatial variability of soil properties that is closely related to soil quality. Scientific interest in the spatial variability in field soils dates back to the early 1890s [10,11], and the study on soil variability in a systematic way was carried out through the 1960s and 1970s [12,13]. Over the years, various indicators have been identified in the literature. E.g. regarding to field-scale soil water properties, the study of Nielsen et al. [14] firstly demonstrated large variations in hydraulic conductivity values and infiltration rates from location to location. Five factors, identified by Xu et al. [15], are critical in determining soil quality, specifically texture, organic matter, porosity, phosphorus and microstructure. Other researches, such as those that apply landscape function analysis (LFA), base their assessments on surface hydrological properties, i.e., infiltration, erosion, rainfall, runoff, plant growth and nutrient cycling [16]. Land evaluation, which attempts to predict the behavior of land for particular uses, is different from soil quality assessment, because the soil biological parameters are not considered in land evaluation [17]. The realistic assessment of CLQ depends on identification of the variables, which is crucial to the sustainable use of regional land resources [1]. With the rapid development in rural society, socioeconomic factors are playing an important role in the land resource system and are more important than that of the natural setting sometimes [1]. Following the FAO framework for land evaluation and the subsequent literature, socioeconomic factors have been more emphasized in land-quality evaluations [18]. For example, changes in extrinsic spatial variability of selected macronutrients (Ca, Mg, K, and P) in surface (sandy) soils that resulted from tillage and fertilizer application were examined by Nkedi-Kizza et al. [19]. Dumanski [20] identified five sets of short-term indicators (nutrient balance, land use intensity, yield trends and variability, land use diversity and land cover) and three sets of long-term indicators (soil quality, agrobiodiversity and land degradation), as well as forest land quality, water quality, land contamination/pollution and range land quality. Thus, considerable research that characterizes intrinsic spatial variability has been performed, whereas fewer studies were carried on the spatial variability of cultivated land caused by extrinsic factors [6]. To improve the understanding on spatial variability in field soils, more studies on addressing extrinsic and intrinsic variations were proposed by Rao and Wagenet [5]. Currently, such studies are still lacking. The equilibrium of land quality is easily disturbed by human activities, such as land consolidation and the behavior of farmers [21]. The effects of these activities are especially noticeable in China in recent years; the overall CLQ has tended to decline and urban areas and the economy have seen rapid development. Because land consolidation is an extremely efficient method of preventing land parcels from being broken up and ameliorating existing difficulties, the consolidative cultivated land (CCL) can be defined as cultivated land being rearranged and taken precautionary measures to ensure more fruitful working of land areas on the basis of developing agricultural technology [22,23]. Scholars and industry have carried out a large number of relevant studies in the past thirty years on the changes in the quality of consolidated cultivated land (CCL) and the factors that drive such changes. For instance, Cay [24] applied the analytic hierarchy process (AHP) to the reallocation process of land consolidation and evaluated the reallocation criteria and results. Caroline et al. [25] quantified and modeled the effect of land consolidation and field borders about soil redistribution in agricultural landscapes. Coelho et al. [26] presented a model that incorporates methods for the evaluation of performing agricultural systems before and after the transformations proposed in land consolidation projects. Tong Luyi [27] explored a method for the evaluation of cultivated land based on a current grading Sustainability 2017, 9, 1312 3 of 21 dataset for achieving a national balance between the requisition and compensation of cultivated land and identifying the impacts of land consolidation. In China, a thematic strategy for the production of cultivated land called “Investigation and Evaluation of Cultivated Land Quality” is currently under development. First, the evaluation area is divided into two parts, depending on whether or not they were to experience land consolidation. In 2014, the 34 national provinces officially launched the evaluation of unconsolidated cultivated land (UCL) at the County level. In 2015, China carried out a comprehensive promotion of the implementation at the provincial level. The most important difference between UCL and CCL is that land consolidation is the main factor that causes the extrinsic spatial variability leading to changes in the CLQ of CCL, but not UCL. Within UCL, the natural factors are intrinsic and relatively stable, whereas the management practices of farmers, economic policies, and other factors maybe the key external driving forces in areas that have not experienced project investment and land consolidation. To date, we do not have a clear understanding of the changes in socioeconomic factors, although they sometimes play a more important role than natural factors. Determining the internal and external factors that affect UCL, particularly the external factors, will help us to improve the land use conditions of CLQ more effectively. Moreover, it is also beneficial to combine UCL with land consolidation projects effectively. How to carry out a systematic evaluation of UCL that considers intrinsic and extrinsic factors has not been extensively studied, and empirical research is lacking. Present work on the monitoring and evaluation of UCL still uses the evaluation system of GALQ that has been used in China since the early 2000s. However, the precision and real-time performance of this system, which is called “Global coverage,” cannot fulfill the requirements of the increasing degree of land resource management because monitoring actual changes in land use conditions is difficult [27]. In addition, the pressure-state-response framework proposed by Dumanski and Pieri [28], which is based on the behavior of farmers, is difficult to employ to perform a regionally comparable assessment of CLQ. Consequently, it is urgent to establish a reliable and applicable evaluation system for assessing the quality of UCL. Therefore, establishing criteria to determine [17] the quality of UCL and to develop indices is increasingly demanded. It may be used to rank and compare the quality of UCL at different locations or at the same location through time [28,29]. Moreover, it’s desirable to evaluate the long-term potential limitations of these criteria and indices, and monitor the short-term changes reacting to land use and management practices [17]. The purpose of this study is to build a comprehensive assessment framework (CAF) of UCL for the south-central and southwestern portions of Hubei Province in China. The specific aims of this study are: (1) to identify the most useful socioeconomic indicators in the assessment by incorporating the natural factors [1]; (2) to design a comprehensive approach to achieve the objectives of land-quality evaluation and monitoring the quality of UCL; and (3) to explore the basic characteristics and spatial distribution of CLQ within the study area, as well as the degree of spatial differentiation, the limiting factors and the reasons underlying these patterns. The focus will be on how to conductive to the sustainability of land use and comprehensive management systems through conceiving the land-protection strategies.

2. Constructing the Evaluation System

2.1. Evaluation Concepts The land-evaluation analysis concentrates on different purposes that can be grouped into two main classes, land vulnerability or degradation approaches and namely land suitability or productivity [17]. The gradation of agricultural land quality (GALQ) framework proposed by the Ministry of Land and Resources of China is the theoretical basis of this paper, which aims to assess land suitability or productivity. The calculation of natural quality grade is consistent with the GALQ method within the study area. In addition, the natural quality factors and their weights, as well as the solar and Sustainability 2017, 9, 1312 4 of 21 temperatureSustainability 2017 potential, 9, 1312 productivity (α) and the yield ratio (β), are kept unchanged. Thus,4 the of 21 land use and production conditions (LUPCs), which have a significant impact on the CLQ, are added [30]. the land use and production conditions (LUPCs), which have a significant impact on the CLQ, are In the process of evaluation, the land use coefficient is generally assessed as a constant value [30]. added [30]. In the process of evaluation, the land use coefficient is generally assessed as a constant Using the concept of coefficient correction, we include the LUPCs (P) in the calculation of the land use value [30]. Using the concept of coefficient correction, we include the LUPCs (P) in the calculation of correction index Kp and reassess the grade of the CLQ (Figure1). P can be converted to the land use the land use correction index Kp and reassess the grade of the CLQ (Figure 1). P can be converted to correctionthe land use coefficient correction using coefficient data obtained using data 2009 obta andined 2014 2009 and and the 2014 corresponding and the corresponding annual scores. annual In the future,scores. whenIn the morefuture, data when are more available, data are it will available, be better it will to usebe better data thatto use cover data a that longer cover time a longer period to calculatetime period the to coefficient. calculate the coefficient.

Evaluationfactors of GALQ

Natural factors of GALQ Supplementary land use factors relating to LUPC

Natural quality evaluation system Land use correction index

Solar and Solar and temperature temperature Land use correction potential potential coefficient (Cp) productivity (α) productivity (β)

Natural Natural quality quality grade gradation index

Original land

use index (Kp)

Land use Land use quality quality grade gradation index

Figure 1. Flowchart used in the evaluation of cultivated land quality (CLQ). Figure 1. Flow chart used in the evaluation of cultivated land quality (CLQ). 2.2. Selection of Assessment Indicators 2.2. Selection of Assessment Indicators It is an essential part of land-evaluation analysis that to select the land characteristics (soil, crop/managementIt is an essential factors part and of climate) land-evaluation as input variables analysis or that diagnostic to select indicators the land for characteristics the predictive (soil, crop/managementmodels, and this selection factors lies and in climate)the issues as under input consideration variables or [17]. diagnostic As mentioned indicators earlier, for the the natural predictive models,(physical and and this chemical) selection quality lies in indicators, the issues such under as considerationorganic matter, [17 soil]. AspH, mentioned irrigation guarantee earlier, the rate, natural (physicaldrainage andcondition, chemical) groundwater quality indicators, depth, obstacle such depth, as organic profile matter, configuration soil pH, and irrigation soil pollution, guarantee are rate, drainageconsistent condition, with the GALQ groundwater and are depth,used to obstacle assess the depth, plain profileareas. In configuration addition, topographic and soil pollution, gradient, are consistentthe amount with of surface the GALQ rock and exposed are used and tothe assess degree the of plainsoil erosion areas. are In addition,used to assess topographic the mountainous gradient, the amountareas. of surface rock exposed and the degree of soil erosion are used to assess the mountainous areas. ToTo revealreveal thethe mechanismsmechanisms by by which which LUPCs, LUPCs, including including macro macro policy, policy, farmer farmer behavior behavior and and urbanization,urbanization, affect CLQ and and to to meet meet the the demand demandss of of land land management management for forCLQ CLQ assessments assessments over over time,time, this this articlearticle usesuses literature analysis analysis [27,30] [27,30 ]an andd expert expert consultation consultation [31] [31 to] choose to choose the theevaluation evaluation indicatorsindicators thatthat supplementsupplement six six LUPC LUPC indicators, indicators, spec specificallyifically infrastructure, infrastructure, traffic traffic conditions, conditions, land land investment intensity, resources, land use type and productivity conditions. The weights are investment intensity, resources, land use type and productivity conditions. The weights are determined determined using the improved AHP, which is a multi-criteria decision-making method [24] (Table Sustainability 2017, 9, 1312 5 of 21 using the improved AHP, which is a multi-criteria decision-making method [24] (Table1). The improved AHP process solves not only the consistency problem of the judgment matrix, but also the problem of convergence speed and accuracy of the knowledge [24,32].

Table 1. Land use and production conditions (LUPCs).

Mountain Plain Target(A) Factor(B) Weight Factor(C) Weight Field form(C1) 0.40 0.40 Infrastructure conditions(B1) 0.20 Farmland continuity(C2) 0.60 0.60 Road network density(C3) 0.60 0.60 Traffic conditions(B2) 0.10 Distance to expressway(C4) 0.40 0.40 Land use and production Intensity of land investment(B3) 0.17 Fertilizer use per unit sown area(C5) 1 1 conditions Area of sloping farmland(≥25◦)(C6) 0.40 / (LUPCs) Resource conditions(B4) 0.18 Arable land per capita(C7) 0.60 0.60 Area of water resources(C8) / 0.40 Method of land use(B5) 0.12 Land use pattern(C9) 1 1 Labor per unit planted area(C10) 0.35 0.35 Productivity conditions(B6) 0.23 Per capita net income of farmers(C11) 0.44 0.44 Gross domestic product(C12) 0.21 0.21

The CLQ evaluation indicators and scoring rules used in assessing UCL are shown in Table2. The scores are determined by Natural breakpoint method to standardize the factor values and assign different grades, which are implemented by ArcGIS. Wei et al. [33] has used the method to divide the investment scale, construction scale, new cultivated land scale and number of projects into four categories [33].

Table 2. Introduction of index and grading rules.

Index Indicator Description Range of Index Values Score [0.79–1.2) 100 It affects the efficiency and convenience of cultivated land (0.79–0.57] 80 Plan form use. The plan form (SHPE) can be calculated using the √ [0.34–0.57) 70 perimeter (C) and area (S) of the plots; SHPE = 4 S/C. <0.34 60 Fields that are contiguous favor maximizing the use of (0.87–1.14] 100 cultivated land and improve the efficiency of agricultural facilities and economies of scale. The contiguity of plot i (0.67–0.87) 80 Contiguity of plots (PC ) can be calculated using the largest (S ) and i max (0.44–0.67) 70 smallest (Smin) plots within the study area and the field area Si; PCi = (Smax − Si)/(Smax − Si). <0.44 60 The area of cultivated land communication is mainly ≥3.17 100 Farmland road affected by farmland road networks. Using the [1.84–3.17) 80 network density administrative village as the statistical unit, the farmland [1.01–1.84) 70 (km/km2) road network density = farmland road (0.29–1.01) 60 length/administrative village land area. <0.29 40 Generally, the agricultural production of the area which is ≥4000 100 adjacent within a certain range of expressway entrances or Distance to exits has a relatively high degree of accessibility. The [1700–4000) 80 expressway (m) negative impacts of expressways without access and (500–1700) 70 under improper operation and management on the land use of the neighboring areas are relatively obvious. <500 60 Paddy fields 100 Refer to the “Second National Land Survey Land use pattern Irrigated land 80 Technical Regulation”. dry land 60 Sustainability 2017, 9, 1312 6 of 21

Table 2. Cont.

Index Indicator Description Range of Index Values Score ≥360 100 The area of water resources, including rivers, lakes, [180–360) 90 Water area (ha) reservoirs, ponds, ditches, inland or coastal glaciers, and [73–180) 80 glaciers and permanent snow cover. (18–73) 60 <18 40 ≥620 20 [290–620) 40 Area of sloping [120–290) 60 The area of cultivated land with a slope of ≥25◦. farmland (ha) [40–120) 80 <40 90 =0 100 Number of people in ≥1.3 100 The labor input per unit area of cultivated land affects the the labor force per unit [0.8–1.3) 80 degree of maturity. The calculation of this index is based of planted area [0.2–0.8) 60 on administrative villages, using statistical data. (person/mu) <0.2 40

Using large quantities of fertilizer leads to soil pollution <30 100 and other problems, resulting in a decline in the quality of Fertilizer use per unit [30–60) 80 cultivated land. The calculation of this index is based on sown area (kg/mu) administrative villages, using data from the Statistical [60–80) 60 Yearbook and the Agricultural Statistics Report. ≥80 40 ≥13,000 100 Per capita net income The calculation of this index is based on administrative [10,000–13,000) 80 of farmers (Yuan) villages and statistical data. [7000–10,000) 60 <7000 40 ≥1.2 100 Per capita cultivated The total area of cultivated land divided by the total [0.7–1.2) 80 land (mu/person) population. [0.18–0.7) 60 <0.18 40 “GDP” means the market value of all final products and ≥300,000 100 Gross domestic services produced by all resident units in a country or [150,000–300,000) 80 product (million Yuan) region for a specified period of time, and it can be [50,000–150,000) 60 obtained by referring to the local Statistical Yearbook. <50,000 40

2.3. Determination of Monitoring Units and Soil Sampling The monitoring units are the basic unit used in the evaluation of UCL management activities. They represent the most detailed means of assessing the effects of UCL management mode on the CLQ. The monitoring unit is composed of fixed and random monitoring units. A fixed monitoring unit (FMU) is a representative unit in the area of UCL that is used to perform monitoring every year. FMUs should be laid out such that they are concentrated within areas of cultivated land, away from roads and cities. They can reflect the features of regional physical geography and agricultural production. In selecting FMUs, priority is given to standard farmland and high-quality basic farmland. Within the area contained by the FMU, a plurality of grading units is selected as the random monitoring unit. The property of the FMU is the average value of the attributes of the FMU and the matched random monitoring unit. Soil sampling is required for each FMU. The scheme for sampling sites was designed to capture the landscape position, variability of landforms, and land uses within the catchment [34].The area of the sampling units ranged between 80 and 100 m2. From each sampling unit, 8 to 10 composite soil samples were collected randomly. The size and homogeneity (hydrologic conditions) of the sampling unit determined the number of composite samples [35]. Soil samples were collected at a depth of 20 cm because most changes that are expected to occur at this depth due to long-term land use management practices [35]. Subsequently, the soil samples were analyzed. The inspected indicators included soil pH and organic matter content. Sustainability 2017, 9, 1312 7 of 21

2.4. Modification of the Grading of Cultivated Land Use On the basis of the physical quality grade index, we modified the land use quality grade index and adopted the same method as GALQ to re-divide the grade of CLQ [30]. The land use modification coefficient of each evaluation unit can be converted using the basis data obtained from the annual scores for 2009 and 2014. In the future, when more data are available, it would be better to use data that cover a longer time period to calculate the coefficients. The land use value (F) of each unit can be calculated using a weighted average model (Equation (4)).

f Kp = ∑ AiBi (1) i=1

f 0 Kp = ∑(Ai0Bi) (2) i=1

Kp Cp = 0 (3) Kp

2 n F = ∑ WB,i ∑ WijFij (4) i=1 j=1 0 In Equations (1)–(3), Kp and Kp are the total cultivated land use value of the years 2014 and 2009 (the first phase of the results of the Second Land Survey of Hubei Province), respectively; Cp is the correction coefficient for cultivated land use; Ai and Ai0 represent the supplemented values of the LUPC indicators for the years 2014 and 2009; and Bi represents the weights associated with the LUPC indicators. In Equation (4), WB,i is the weight of factor i; Wij and Fij are the weight and score of factor j under a given level of factor i; and n is the number of factors, given the level of the corresponding factors.

3. Application of the Evaluation System

3.1. Study Area The study area includes the south-central plain division (SCPDHP) and the southwest mountainous division of Hubei Province (SWMDHP) in China (Figure2). These are as were chosen because they display contrasting biophysical, topographic and socioeconomic conditions [36] (Figure3), which are representative of the cultivated land system in Hubei Province and most parts of China. The SCPDHP is an area of intensive agriculture with inherently humic top soils, high rainfall and substantial socioeconomic opportunities and population density [36]. In contrast, the SWMDHP displays lower agricultural potential, poor soils, lower socioeconomic opportunities and a sparser population distribution. The SCPDHP is located in the middle reaches of the Yangtze River and is a typical alluvial lacustrine plain. It mainly includes most of the Jianghan Plain and covers 15 counties, districts or County-level cities including Jingzhou and other cities. The zonal soil is yellow-brown soil, the terrain is flat, the lake is densely covered, and the river network is interwoven. The region experiences favorable temperatures and rainfall conditions, and the CLQ is higher than that of other regions in China. It is greatly affected by the development of urbanization since it is located in the hinterland of the metropolitan area of Wuhan, and it faces the dilemma of economic development and farmland protection [37]. According to the Statistical Yearbook of Hubei Province, the urbanization rate of the Jianghan Plain increased from 9.52% in 1980 to approximately 43.89% in 2010, and the area of cultivated land decreased by approximately 15.29% [37]. SustainabilitySustainability 20172017,, 99,, 13121312 88 ofof 2121

TheThe SCPDHPSCPDHP isis locatedlocated inin thethe middlemiddle reachesreaches ofof thethe YangtzeYangtze RiverRiver andand isis aa typicaltypical alluvialalluvial lacustrinelacustrine plain.plain. ItIt mainlymainly includesincludes mostmost ofof thethe JianghanJianghan PlainPlain andand coverscovers 1515 counties,counties, districtsdistricts oror County-levelCounty-level citiescities includingincluding JingzhouJingzhou andand otherother citicities.es. TheThe zonalzonal soilsoil isis yellow-brownyellow-brown soil,soil, thethe terrainterrain isis flat,flat, thethe lakelake isis denselydensely covered,covered, andand thethe ririverver networknetwork isis interwoven.interwoven. TheThe regionregion experiencesexperiences favorablefavorable temperaturestemperatures andand rainfallrainfall conditions,conditions, andand thethe CLQCLQ isis higherhigher thanthan thatthat ofof otherother regionsregions inin China.China. ItIt isis greatlygreatly affectedaffected byby ththee developmentdevelopment ofof urbanizationurbanization sincesince itit isis locatedlocated inin thethe hinterlandhinterland ofof thethe metropolitanmetropolitan areaarea ofof Wuhan,Wuhan, andand itit facesfaces ththee dilemmadilemma ofof economiceconomic developmentdevelopment andand farmlandfarmland protectionprotection [37].[37]. AccordingAccording toto thethe StatisticalStatistical YearbookYearbook ofof HubeiHubei Province,Province, thethe urbanizationurbanization raterate ofof thethe SustainabilityJianghanJianghan PlainPlain2017, 9 ,increasedincreased 1312 fromfrom 9.52%9.52% inin 19801980 toto approximatelyapproximately 43.89%43.89% inin 2010,2010, andand thethe areaarea8 of of 21of cultivatedcultivated landland decreaseddecreased byby approximatelyapproximately 15.29%15.29% [37].[37].

((AA))

Hubei province Hubei province Southwestern Southwestern South-centralSouth-central part of Hubei part of Hubei partpart ofof HubeiHubei province provinceprovince province

((BB))

Figure 2. General situation of the study area: (A) the location of Hubei Province in China; and (B) area Figure 2. General situation of the study area: ( A) the location of Hubei Pr Provinceovince in China; and (B) area map of the south-central plain division and southwest mountainous division. map of the south-central plain divisiondivision andand southwestsouthwest mountainousmountainous division.division.

SCPDHPSCPDHP andand SWMDHPSWMDHP

Figure 3. Topographic map of the study area. FigureFigure 3.3. TopographicTopographic mapmap ofof thethe studystudy area.area.

TheThe SWMDHPSWMDHPSWMDHP is isis located locatedlocated in inin the thethe northeastern northeasternnortheastern part partpart of the ofof Wuling thethe WulingWuling Mountains MountainsMountains in south-central inin south-centralsouth-central China, andChina,China, it represents andand itit representsrepresents the second thethe ladder secondsecond to ladderladder the transition toto thethe tr zonetransitionansition of the zonezone third ofof ladder thethe thirdthird [38]. ladderladder It contains [38].[38]. 19 ItIt counties,containscontains districts or County-level cities of Enshi and Yichang City. The landscape of the area is generally rugged, and the altitude [35] ranges from 800 to 1000 m above sea level. The land use is predominantly agricultural and is planted with crops; however, the percentages of cover with trees, orchards, and tea vary. The major soil types are mountain yellow soil and mountain yellow-brown soil [39]. Conditions within the study area are warm in winter and cool in summer, the amount and distribution of rainfall are predictable, and the annual precipitation ranges [40] from 1100 to 1800 mm [39]. The geographical and climatic conditions differ from those of other regions that are representative of the central and southern mountainous areas of China [41].The region contains a national-level concentration of contiguous poor areas and a high number of poverty-stricken counties. In addition, the level of agricultural Sustainability 2017, 9, 1312 9 of 21 mechanization is low and traffic conditions are relatively backward within the region. These factors influence the relationship between environmental changes and household behaviors [42].

3.2. Data Collection and Processing

3.2.1. Data Collection The data used in this study are mainly derived from the “Investigation and Evaluation of Cultivated Land Quality in Hubei Province” project, which was conducted in 2014 by the Department of Land and Resources of Hubei Province. This project provided results for a range of UCL in the study area, including CLQ grades, natural quality factor values, monitoring unit locations and attributes, and crop yields; however, the LUPCs are not provided. Additional investigations need to be carried out. The supplemental survey data regarding this project can be obtained from two major sources, specifically the regional Statistical Yearbooks and land use change survey (LUCS) maps. The regional Statistical Yearbooks provide aggregated data for different administrative units which were collected by individual counties [43]. The datasets provide time series of actual data including indicators, such as population, the area of cultivated land, the amount of fertilizer applied, the number of people in the labor force, and the net income of farmers. The limitations of such data include the relatively coarse size of the administrative units and the methods of data collection used in different counties [43]. In contrast, it’s more easily applied consistently for the methods been used to derive LUCS map sources, and these maps provide greater spatial detail [43]. These data may represent an important first step in improving our understanding of the spatial distribution of LUPCs within the study area.

3.2.2. Data Processing

Delineate the Scope of Evaluation Using the method of spatial super position built into GIS software, based on the map of the LUCS database in 2014, we cut out the following two parts to determine the scope of the UCL. • The newly increased cultivated land. This quantity includes two parts. One part represents the supplementary cultivated land due to land consolidation, whereas the other represents increases that have occurred within the past three years that were caused by the self-development of farmers, adjustments in the agricultural structure or other causes. Newly cultivated land areas can be extracted according to the results of the LUCS map covering nearly three years. The scope of the project area and the final materials shall be subject to approval. • Reductions in cultivated land. In the past three years, the area of cultivated land has decreased due to construction, disasters, adjustment in agricultural structure and ecological restoration. According to the LUCS map results for 2012 and 2014, the reductions in cultivated land areas are extracted. Finally, the attribute data of the updated evaluation units for 2013are transferred into the cultivated land units in 2014 using GIS software (Figure4). SustainabilitySustainability 20172017,, 99,, 13121312 1010 ofof 2121

SustainabilityFinally,Finally,2017 thethe, 9, 1312attributeattribute datadata ofof thethe updatedupdated evaluationevaluation unitsunits forfor 2013are2013are transferredtransferred intointo10 ofthethe 21 cultivatedcultivated landland unitsunits inin 20142014 usingusing GISGIS softwaresoftware (Figure(Figure 4).4).

FigureFigure 4.4. SpatialSpatial distribution distribution of of UCL. UCL.

AcquisitionAcquisition ofof EvaluationEvaluation IndexIndex ValueValue AccordingAccording toto thethe supplementarysupplementary land land use use evaluation evaluation index index (Table (Table1 1),the1),the),the area areaarea of ofof cultivated cultivatedcultivated land landland perper capitacapita (C7),(C7), GDPGDP (C12)(C12) andand otherother indicators indicators (C5,(C5, C10,C10, andand C11) C11) can can be be obtained obtained from from the the Statistical Statistical Yearbook,Yearbook, andand fieldfieldfield formformform (C1),(C1),(C1), farmlandfarmland continuitycontinuity (C2),(C2), roadroad networknetwork densitydensity (C3) (C3) and and thethe otherother fourfour indexes indexes (C4, (C4, C6, C6, C8, C8C8, and andand C9)C9)C9) cancancan bebebe derivedderivedderived usingusinusingg thethe analysisanalysis toolstools withwith inin the the ArcGIS ArcGIS software software package,package, includingincluding overlayoverlay analysis,analysis, extraction extraction analysis analysis and and field fieldfield analysis.analysis. The The distance distance and and area area toto thethe nearest nearest neighborneighbor cancan bebe calculated calculated using using the the spatial spatial statistics statistics tool. tool. In In this this paper, paper, the the research research unit unit includes34includesincludes34 34 counties,counties, counties, districtsdistricts districts oror or CoCo County-levelunty-levelunty-level cities cities withinwithin thethe study study area. Ultimately, factorfactor valuesvalues werewere obtainedobtained forfor 3434 countiescountiescounties usingusingusing datadatadata fromfromfrom 747 747747 FMUs FMUsFMUs (Figure (Figure(Figure5 5).).5).

FigureFigure 5. 5. SpatialSpatial distribution distribution of of fixed fixedfixed monitoring monitoring unitsunits (FMUs).(FMUs). Sustainability 2017, 9, 1312 11 of 21 Sustainability 2017, 9, 1312 11 of 21

3.3. Results and Discussion

3.3.1. Basic Characteristics of CLQ within the Study AreaArea According to the GALQ results for ChinaChina issued by the MinistryMinistry of LandLand andand ResourcesResources in December 2009,2009, the the agricultural agricultural land land within within China China was divided was divided into 15 categoriesinto 15 categories (primarily (primarily categories 7–13).Thecategories mean7–13).The classification mean classi calculatedfication calculated using area using weighted area weighted averaging averaging (CC-AWA) (CC-AWA) was 9.8 [was44]. Assessment9.8 [44]. Assessment of the study of the area study showed area showed that the gradesthat the of grades CLQ presentof CLQ withinpresent the within study the area study in 2014area werein 2014 5–9 were and 5–9 the and CC-AWA the CC-AWA was 7.6, was which 7.6, indicateswhich indicates that the that overall the overall level oflevel CLQ of inCLQ the in study the study area wasarea moderatewas moderate and slightly and slightly higher higher than the than national the national average. average. Among theAmong 34 counties the 34 ofcounties the SCPDHP of the andSCPDHP the SWMDHP, and the SWMDHP, the counties the with counties the three with bestthe th overallree best optimal overall average optimal classifications average classifications are Zigui andare Zigui Xiaoting and withinXiaoting the within SWMDHP the SWMDHP and Gongan and within Gongan the within SCPDHP, the SCPDHP, which have which values have of 2.9,values 4.1 andof2.9, 4.5, 4.1 respectively. and 4.5, respectively. The three counties The three with counti the worstes with average the classificationworst average values classification areLaifeng, values Enshi andareLaifeng, Badong Enshi within and the SWMDHP,Badong within which the have SWMDHP, values of 12.6,which 13 andhave 13.1, values respectively. of 12.6, Excellent13 and 13.1, and high-qualityrespectively. UCLExcellent occurs and primarily high-quality within UCL the occurs SCPDHP. primarily The lower within part the is SCPDHP. mainly distributed The lower withinpart is themainly SWMDHP distributed (Figure within6). the SWMDHP (Figure 6).

Figure 6. Comparison of the average quality grade of UCL within the study area. Figure 6. Comparison of the average quality grade of UCL within the study area.

The quality of cultivated land can be divided into four classes, based on their gradation: excellent (1–4),The high quality (5–8), of moderate cultivated (9–12) land and can below divided (13–15) into [44]. four The classes, area covered basedon bytheir cultivated gradation: land excellentwith the (1–4),excellent high grade (5–8), is moderate 968.0 kha, (9–12) which and accounts low (13–15) for 14.9% [44]. of The the area total covered area; the by high cultivated grade is land inferred with thefor excellent3589.5 kha, grade which is 968.0accounts kha, for which 55.3% accounts of the total for 14.9% area; and of the the total areas area; of the the moderate high grade and is inferredlow grades for 3589.5are 1648.8 kha, whichkha and accounts 285.0 forkha, 55.3% and of these the total grades area; account and the areasfor 25.4% of the and moderate 4.4% andof the low total grades area, are 1648.8respectively. kha and The 285.0 statistics kha, and of the these four grades classes account of cultivated for 25.4% land and at 4.4%the County of the totallevelarea, (Figures respectively. 7 and 8, Theand statisticsTable 3) reveal of the that four the classes structure of cultivated of the gradat landion at differs the County among level the counties (Figures 7within and8 ,the and study Table area.3) revealFigure that the 7 compares structure the of the calculation gradation results differs from among thethe conventional counties within GALQ the method study area.(without using socioeconomicFigure7 compares factors) and the calculationthe proposed results CAF fromapproach. the conventional Apparently, GALQGALQ methodwould overestimate (without using the socioeconomicland quality, where factors) part and of the cultivated proposed CAFland approach.grades (CLG) Apparently, of SCPDHP GALQ are wouldin the range overestimate of excellent the land(1–4), quality, which fall where into part the ofmoderate the cultivated quality land range grades (5–8) (CLG)in the CAF of SCPDHP evaluation. are inOn the the range other of aspect, excellent the (1–4),CLG of which SWMDHP fall into in the the moderate low range quality (13–15) range derived (5–8) from in the CAF CAF is evaluation.much wider On than the that other from aspect, GALQ. the CLGConsequently, of SWMDHP the integration in the low rangeof the (13–15)land use derived evalua fromtion factors CAF is and much correction wider than coefficient that from should GALQ. be Consequently,more comprehensive the integration to reflect of the the impa landct useof land evaluation use conditions factors andof CLQ. correction coefficient should be more comprehensive to reflect the impact of land use conditions of CLQ. Sustainability 2017, 9, 1312 12 of 21

Table 3. Structure of the grade of cultivated land of the 34 counties in the study area.

Excellent High Moderate Excellent High Moderate Low County (District, City) Low (kha) County (District, City) (kha) (kha) (kha) (kha) (kha) (kha) (kha) Lichuan — 0.1 110.8 — Dangyang — 0.0 180.4 22.0 Xianfeng — — 47.7 — Zhijiang — 142.4 49.8 — Laifeng — — 42.2 34.7 Songzi 14.8 239.0 0.1 — Xuanen — 22.1 98.3 — Shashi 4.3 71.2 — — Enshi — — 66.5 68.8 Jingzhou 89.2 379.2 — — Jianshi 31.8 120.2 13.3 — Jianglin 263.5 206.9 — — Hefeng — — 14.8 7.8 Gongan 82.9 36.9 — — Badong — — 75.6 138.7 Jianli — 388.3 238.3 — Wufeng — — 34.8 7.7 Shishou — 129.5 218.0 — Changyang — — 48.1 5.3 Honghu — 51.8 13.9 — Xiling — 0.0 0.1 — Qianjiang 115.3 644.9 222.7 — Dianjun — 1.0 5.0 — Xiantao 173.1 750.5 15.9 — Xiaoting 2.2 1.8 — — Tianmen 96.6 92.0 — — Wujiagang — 0.1 0.1 — Hanchuan 2.5 154.9 10.9 — Yiling — 65.6 32.3 — Jiayu — 33.5 — — Zigui 91.9 18.1 — — Subtotal for the SCPDHP 842.1 3320.7 949.9 22.0 Yuanan — 18.6 60.2 — Total 968.0 3589.5 1648.8 285.0 Xingshan — 11.9 6.1 — Yidu — 9.5 43.7 — Subtotal for the SWMDHP 125.9 268.8 699.0 263.0 Sustainability 2017, 9, 1312 13 of 21 Sustainability 2017, 9, 1312 13 of 21 Sustainability 2017, 9, 1312 13 of 21

(A) (B) (A) (B) Figure 7. Spatial distribution of UCL grade in 2014: (A) result of GALQ; and (B) result of CAF. Figure 7.7. Spatial distribution of UCL gradegrade inin 2014:2014: (A)) resultresult ofof GALQ;GALQ; andand ((BB)) resultresult ofof CAF.CAF.

Figure 8. Structure of the grade of cultivated land ofthe34 counties. FigureFigure 8.8. StructureStructure ofof thethe gradegrade ofof cultivatedcultivated landland ofthe34ofthe34 counties.counties. The Excellent and High Grades Are Mainly Located in the SCPDHP TheThe ExcellentExcellent andand HighHigh GradesGrades AreAre MainlyMainly LocatedLocated inin thethe SCPDHPSCPDHP The top 87.0% of cultivated land is distributed in the SCPDHP, and the excellent grade is only foundTheThe in Jianshi toptop 87.0%87.0% County, ofof cultivatedcultivated Xiaoting land landDistrict isis distributeddistributed and Zigui County in the SCPDHP,SCPDHP, of SWMDHP. andand thethe There excellentexcellent are fifteen gradegrade counties isis onlyonly infoundfound the SCPDHP, inin JianshiJianshi County,ofCounty, which Xiaoting nineXiaoting contain District District excellent and and Zigui Zigui cult Countyivated County land of of SWMDHP. (gradesSWMDHP. 1–4). There There However, are are fifteen fifteen the counties total counties area in ofthein excellentthe SCPDHP, SCPDHP, cultivated of whichof which nineland nine containis containsmall. excellent Only excellent the cultivated threecultivated counties land land (grades of(grades Jiangling, 1–4). 1–4). However, QianjiangHowever, the andthe total total Xiantao area area of containexcellentof excellent areas cultivated cultivated of excellent land land iscultivated small. is small. Only land Only the larger three the than countiesthree 100kha. counties of Jiangling, Jingzhou of Jiangling, Qianjiang District, Qianjiang Gongan and Xiantao andCounty containXiantao and Tianmenareascontain of excellentareas are three of excellent cultivated counties cultivated with land areas larger land of than excellent larger 100 kha.than cultivated Jingzhou100kha. land Jingzhou District, of 80 Gongan District,to 100 kha,County Gongan whereas and County Tianmenthe area and ofareTianmen excellent three countiesare cultivated three with counties areasland within of excellentSongzi areas isof cultivated 15excellent kha, and landcu ltivatedthe of remaining 80 to land 100 of kha, Shashi80 whereasto 100 District kha, the whereas areaand ofHanchuan excellent the area containcultivatedof excellent areas land cultivated of inexcellent Songzi land iscultivated 15 in kha, Songzi and land is the that15 remaining kha, are lessand than Shashithe remaining5 kha District in size. andShashi Hanchuan District containand Hanchuan areas of excellentcontainMeanwhile, areas cultivated of excellent92.5% land of that cultivatedthe arehigh less grade land than cultivatedthat 5 kha are in less size. la ndthan (grades 5 kha in5–8) size. is distributed in the SCPDHP, with Meanwhile,Meanwhile,a large area 92.5%92.5%of 3320.7 ofof thethe kha. highhigh The gradegrade areas cultivated ofhigh grade landland (gradescultivated 5–8) land is distributed in Xiantao in and the Qianjiang SCPDHP, Countywithwith aa largelargeare greater areaarea of ofthan 3320.73320.7 600 kha.kha,kha. whereas TheThe areasareas the ofhighofhigh areas gradeofgrade high cultivatedcultivated grade cultivated landland inin land XiantaoXiantao in the andand 4 counties QianjiangQianjiang of Jianli,CountyCounty Jingzhou, areare greatergreater Songzi thanthan and 600600 Jiangling kha,kha, whereaswhereas are more thethe areasareas than of200of highhigh kha. gradegrade The areas cultivatedcultivated of high landland grade inin thethe cultivated 44 countiescounties land ofof inJianli,Jianli, the Jingzhou,otherJingzhou, counties SongziSongzi all and andfall JianglingwithinJiangling the areare range moremore of thanthan 30–60 200200 kha, kha.kha. with TheThe the areasareas exception ofof highhigh gradegradeof Dangyang, cultivatedcultivated which landland doesinin thethe not otherother contain countiescounties high-grade allall fallfall withinwithincultivated thethe rangeland.range Althou ofof 30–6030–60gh kha, kha,high withwith grade thethe cultivated exceptionexception land ofof Dangyang,Dangyang, occursin 58% whichwhich of thedoesdoes counties notnot containcontain in the high-gradehigh-grade SWMDHP, cultivatedcultivated the areas land.areland. small. AlthoughAlthou Thegh exceptions highhigh gradegrade include cultivatedcultivated Jianshi landland County occursinoccursin and 58%58%Yiling ofof District,thethe countiescounties which inin contain thethe SWMDHP,SWMDHP, more than thethe areas100areas kha areare and small.small. more TheThe than exceptionsexceptions 50 kha of includeinclude high grade JianshiJianshi cultivated CountyCounty andandland. YilingYiling The otherDistrict, counties which include contain less more than than 30 kha100 khaof high and grade more cultivatedthan 50 kha land. of high grade cultivated land. The other counties include less than 30 kha of high grade cultivated land.

Sustainability 2017, 9, 1312 14 of 21

District, which contain more than 100 kha and more than 50 kha of high grade cultivated land. The other counties include less than 30 kha of high grade cultivated land.

Moderate Grade Cultivated Land Is Distributed Widely, and Low Grade Cultivated Land Is Mainly Distributed in the SWMDHP The land assessment shows that 42.4% of the moderate grade cultivated land is found within the SWMDHP, whereas 57.6% is found within the SCPDHP. However, the distribution is relatively balanced in the SWMDHP, and the moderate grade cultivated land is found in most of the 17 counties except Xiaoting District and Zigui County. Among the 17 counties, the largest area is 110.8 kha, and the average size is 36.8 kha. The moderate grade cultivated landis more concentrated in the SCPDHP. Moderate grade cultivated land is distributed in nine counties in the SCPDHP, including Dangyang City, Jianli County, Shishou City and Qianjiang City, which contain areas of moderate grade cultivated land greater than 180 kha. Within these counties, the maximum area of moderate grade cultivated land is 238.3 kha, and the average area of moderate grade cultivated land is 63.3 kha. 92.3% of the low grade cultivated land occurs within the SWMDHP. The area of low grade cultivated land within Badong Countyis more than 100 kha, whereas the corresponding areas within Enshi City and Laifeng County are 30–70 kha. The areas of low grade cultivated land within the remaining three counties are 5–10 kha. In the SCPDHP, low grade cultivated land is found only in Dangyang City, which is bounded to the east by the SWMDHP.

The Distribution of the Different Grades of Cultivated Land across Counties and within Individual Counties The distribution of the different grades of cultivated land across counties and within individual counties is complex (Figure9). First, the degree of concentration of cultivated land differs among the counties, which is reflected in the difference between the best and worst monitoring units found in each of the counties. For example, in the counties of the SWMDHP, such as Jianshi and Xiaoting, and in the counties of the SCPDHP, such as Qianjiang and Xiantao, the gap between the highest and the lowest monitoring units is more than seven on average, compared to less than three for Badong County in the SWMDHP and Shishou City and Jiayu County in the SCPDHP. For the SCPDHP, the gap between the highest and the lowest monitoring units is slightly larger than that of the SWMDHP, and these gaps are 4.9 and 4.5, respectively. Second, the deviation of the average quality grade between the counties and the study area is quite different. For instance, counties such as Laifeng, Enshi, and Badong in the SWMDHP are five grades lower than the average level for the study area (AL-SA). In contrast, Gongan County in the SCPDHP is three grades higher than the AL-SA. Generally, the CLQ in the SCPDHP is better than that of the regional average, whereas those in the SWMDHP are lower than the regional average. Third, the deviation of the average grade of the counties is different from that within the counties. For example, the average grade is one higher than the mean grade for Tianmen City, while the average grade is 0.3–0.5 lower than the mean grade for Zhijiang City and Gongan County. This result shows that greater deviations between the average and mean grades were noted in the SCPDHP, and the width of the distribution in the SCPDHP is greater than that in the SWMDHP. This variability could be owing to spatial differences in the natural processes that have shaped the landscape, as well as land management practices [4]. Sustainability 2017, 9, 1312 15 of 21 Sustainability 2017, 9, 1312 15 of 21

Figure 9. The highest, lowest and average grades of UCL quality in the counties within the study area. Figure 9. The highest, lowest and average grades of UCL quality in the counties within the study area. 3.3.2. Variation in LUPCs across the Study Area 3.3.2. Variation in LUPCs across the Study Area Values of several descriptive statistics, specifically the mean, maximum, minimum, standard Valuesdeviation of (SD), several coef descriptiveficient of variation statistics, (CV) specifically and skew ness, the mean,were calculated maximum, for minimum,measuring land standard deviationproperties (SD), [4]. coefficient The CV was of calculated variation to (CV) enable and un skewderstanding ness, of were the spatial calculated variability for measuring of the land land propertiescharacteristics. [4]. The Generally, CV was calculated land characteristics to enable ca understandingn be expressed by of thedividing spatial the variability CV into different of the land characteristics.ranges [45]. Generally,E.g., a CV less land than characteristics 10% indicates canminimal be expressed variability, by a CV dividing less than the 100% CV intoindicates different moderate variability, and a CV that exceeds 100% reflects intense variability [46]. ranges [45]. E.g., a CV less than 10% indicates minimal variability, a CV less than 100% indicates Based on the County level, the coefficient of variation of 12 land use factors were calculated moderate variability, and a CV that exceeds 100% reflects intense variability [46]. using data collected at FMU sin the SCPDHP and SWMDHP (Tables 4 and 5). In total, 385 and 362 BasedFMUs were on the set Countyup in the level, SCPDHP the CVand ofthe 12 SWMDHP land use, respectively. factors were After calculated eliminating using the dataabnormal collected at FMUvalues sin of the 0 that SCPDHP were obtained and SWMDHP for some (Tablesindexes,4 336 and values5). In remained total, 385 for and the 362 SCPDHP, FMUs whereas were set 337 up in the SCPDHPvalues were and retained the SWMDHP, for the SWMDHP. respectively. After eliminating the abnormal values of 0 that were obtainedThere for some is obvious indexes, differences 336 values in the remained land properties for the SCPDHP,between the whereas SCPDHP 337 and values the SWMDHP. were retained for theThe SWMDHP. SCPDHP displays higher values for C1, C2, C3, and C5 and most of the LUPCs compared to the ThereSWMDHP. is obvious Using the differences CV to express in the variability land properties [4], C8, C10 between and C12 the were SCPDHP the most and variable the SWMDHP. (≥100%) The SCPDHPLUPCs displays for the SCPDHP. higher valuesModerate for variability C1, C2, C3,(CV and50%–100%) C5 and was most observed of the for LUPCs C4, C5 and compared C7, while to the C1, C2, C3, C9 and C11 displayed the least variability (CV < 50%). For the SWMDHP, C4, C6, C8, C10 SWMDHP. Using the CV to express variability [4], C8, C10 and C12 were the most variable (≥100%) and C12 were the most variable LUPCs; C3 and C7 displayed moderate variability; and C1, C2, C5, LUPCsC9 forand the C11 SCPDHP. displayed Moderateminimal variability. variability (CV 50–100%) was observed for C4, C5 and C7, while C1, C2, C3, C9 and C11 displayed the least variability (CV < 50%). For the SWMDHP, C4, C6, C8, C10 and C12 were the most variableTable 4. LUPCs; Descriptive C3 statistics and C7 for displayed the LUPCs moderate of the SCPDHP. variability; and C1, C2, C5, C9 and C11 displayed minimal variability.Number of LUPC Monitoring Minimum Maximum Mean SD CV% Table 4. DescriptiveUnits statistics for the LUPCs of the SCPDHP. Field form (C1) 336 0.34 1.02 0.7746 0.16569 21.4 Farmland continuity(C2) 336 0.04 1.7500 0.8067 0.18686 23.2 Number of Road network density(C3) 336 0.125 9.360 3.6010 1.634488 45.4 LUPC Monitoring Minimum Maximum Mean SD CV% Distance to expressway(C4) 336 7.01 8730.47 1744.6 1.69001 96.9 Units The amount of chemical fertilizer 336 3.57 183.74 65.569 45.9596 70.1 appliedField per form unit (C1)sown area (C5) 336 0.34 1.02 0.7746 0.16569 21.4 FarmlandCultivated continuity land per capita (C2) (C7) 336 336 0.040.02 1.7500 5.6000 0.8067 1.7388 0.18686 1.03307 59.423.2 RoadArea network of water density resources (C3) (C8) 336 336 0.125 0.80 1102.26 9.360 3.6010 105.89 1.634488 127.4116 120.345.4 DistanceLand to expresswayuse pattern(C9) (C4) 336 336 60.007.01 8730.47 100.00 1744.6 89.642 1.69001 14.737 16.496.9 The amount of chemical fertilizer Laborper unit planted area(C10) 336 336 0.004 3.57 183.74 1.1700 65.569 0.1270 45.9596 0.13854 109.170.1 appliedPer capita per net unit income sown of area farmers(C11) (C5) 336 5650.00 20,119.00 12,480.7 1963.748 15.7 CultivatedGross domestic land per product(C12) capita (C7) 336 336 8081.00 0.02 1,050,910.00 5.6000 203,294.54 1.7388 1.03307 2.2874 112.559.4 Area of water resources (C8) 336 0.80 1102.26 105.89 127.4116 120.3 Land use pattern (C9) 336 60.00 100.00 89.642 14.737 16.4 Laborper unit planted area (C10) 336 0.004 1.1700 0.1270 0.13854 109.1 Per capita net income of farmers (C11) 336 5650.00 20,119.00 12,480.7 1963.748 15.7 Gross domestic product (C12) 336 8081.00 1,050,910.00 203,294.54 2.2874 112.5 Sustainability 2017,, 9,, 13121312 16 of 21

Table 5. Descriptive statistics for the LUPCs of the SWMDHP. Table 5. Descriptive statistics for the LUPCs of the SWMDHP. Number of LUPC Monitoring Minimum Maximum Mean SD CV% NumberUnits of LUPC Monitoring Minimum Maximum Mean SD CV% Field form (C1) Units 337 0.08 0.97 0.5339 0.18345 34.4 Farmland continuity(C2) 337 0.03 0.99 0.637122 0.246160 38.6 Field form (C1) 337 0.08 0.97 0.5339 0.18345 34.4 Road network density(C3) 337 0.06 11.27 2.17522 1.29476 59.5 Farmland continuity (C2) 337 0.03 0.99 0.637122 0.246160 38.6 DistanceRoad network to expressway(C4) density (C3) 337 337 0.06 5.7 3,7230.14 11.27 6400.183 2.17522 8686.318 1.29476 135.7 59.5 The amount of chemical fertilizer Distance to expressway (C4) 337337 5.08 5.7 103.26 37,230.14 36.1073 6400.183 16.8964 8686.318 135.746.8 appliedThe amount per unit of chemical sown area fertilizer (C5) 337 5.08 103.26 36.1073 16.8964 46.8 Areaapplied of sloping per unit farmland( sown area≥25°) (C5) (C6) 337 0 1550 203.5152 320.6862 157.6 ◦ AreaCultivated of sloping land farmland( per capita≥25 (C7)) (C6) 337 337 0.04 0 3.33 1550 1.25095 203.5152 0.6601788 320.6862 157.6 52.8 CultivatedArea of water land resources(C8) per capita (C7) 337 337 0.140.04 768.42 3.33 33.0436 1.25095 69.731870.6601788 211.0 52.8 Area of water resources (C8) 337 0.14 768.42 33.0436 69.73187 211.0 Land use pattern(C9) 337 60 100 79.5252 19.69439 24.8 Land use pattern(C9) 337 60 100 79.5252 19.69439 24.8 LaborLabor per unit planted areaarea(C10) (C10) 337 337 0.04 3.42 3.42 0.415252 0.415252 0.6170586 0.6170586 148.6 PerPer capita capita net income ofof farmersfarmers(C11) (C11) 337 337 6164 16,630 16,630 9849.3472 9849.3472 3231.588 3231.588 32.8 GrossGross domestic productproduct(C12) (C12) 337 337 14,796 14,796 1,813,960 1,813,960 205,795.10 205,795.10 308,806 308,806 150.1

The data analysis presented in Tables 4 4 and and5 5reveals reveals that that the the CVs CVs of of the the LUPCs LUPCs in in the the SCPDHP SCPDHP range from from 15.7% 15.7 to to 120.3%, 120.3%, where where asas thosethose ofof thethe SWMDHP range range from from 24.8% 24.8 to to 211.0%. 211.0%. TheThe CVs calculated for the two divisions all reflect reflect intense and moderate variability, according to generally accepted standards. standards. The The greatest greatest variability variability for for the the SCPDHP SCPDHP and and the the SWMDHP SWMDHP are areobserved observed for C8, for C10C8, C10and andC12, C12, whereas whereas the minimal the minimal variability variability for the for two the twodivisions divisions are observed are observed for C1, for C2, C1, C9 C2, and C9 C11.and C11.These These results results show show that that the the infrastructure infrastructure an andd traffic traffic conditions conditions and and especially especially the the resource conditions andand economic economic development development levels levels of the of counties the counties in each in of theeach two of districts the two are districts unbalanced. are unbalanced.Moreover, the Moreover, spatial differences the spatial in differences the CVs in in the the mountainous CVs in the mountainous areas are clearly areas greater are clearly than greater that in thanthe plain that areasin the (Figure plain areas 10). (Figure 10).

250

200

150

100

50

0 C7C12C11C5C10C1C2C3C4C8C9C6

CV of SCPDHP % CV of SWMDHP %

FigureFigure 10.10. ComparisonComparison of the of theCV CVof the of land the land use factor use factorss between between the SCPDHP the SCPDHP and the and SWMDHP the SWMDHP in 2014. in 2014. Owing to the very complicated nature of soil, which depends on its physical, chemical and biologicalOwing properties to the very and complicated their interactions, nature the of soil,accuracy which of dependssoil quality on assessment its physical, is chemicalincreased and by usingbiological as many properties indicators and their as possible interactions, [47]. theThis accuracy variability of soilmay quality be due assessment to spatial isdifferences increased byin geographicalusing as many location, indicators as well as possibleas land [management47]. This variability practices may[4]. Different be due to areas spatial display differences different in naturalgeographical properties location, and management as well as land practices management [48]. Mo practicesmtaz et al. [4 [48]]. Different and Seyfried areas et display al. [49] observed different thatnatural the propertiesdistribution and and management variability of practices land prop [48].erties Momtaz depend et al. on [48 the] and scale—the Seyfried bigger et al. [the49] scale, observed the

Sustainability 2017, 9, 1312 17 of 21 that the distribution and variability of land properties depend on the scale—the bigger the scale, the higher the variability, and vice versa [4]. For all that, the variability provided a better view of the status of the soil across the study area [4].

3.3.3. Analysis of the Limiting Factors of the Comprehensive CLQ To facilitate the division of restricted types, the LUPCs were divided into categories that reflect utilization convenience (UC) (including C5, C7, C10, C11 and C12) and utilization stability (US) (including C1, C2, C3, C4, C6 or C8 and C9). Comparative analysis of the moderate (grades 9–12) and low (grades 13–15) grades of cultivated land for the natural quality and LUPC scores indicate that the comprehensive quality of the study area of UCL in terms of three single levels (natural quality, UC and US) and two combinatorial levels (natural and UC, natural and US) is limited. Of these levels, for the cultivated land (CL) that is limited by natural conditions, the comprehensive quality is mainly affected by organic matter, soil pH values and groundwater depth, as well as GDP and labor, while natural factors play a major role (Table6). The CL that is limited by UC is affected by obstacle depth, the irrigation guarantee rate and C1, C2, C4, C8 and C10. The CL that is limited by US is substantially influenced by soil pH, the irrigation guarantee rate and C10, C11 and C12. However, the two combinations of the restricted types of cultivated land are limited by different levels and factors. This interaction interprets the intricate relationship between improving soil fertility and LUPCs in the UCL area. The variations observed across studies depend on specific soil-forming processes, land conditions and management-related factors [50]. Further analysis shows that groundwater depth, obstacle depth and profile configuration are affected by regional physical and geographical conditions, which are difficult to improve in the short term. Organic matter, soil pH and the irrigation guarantee rate should be improved as soon as possible through changing the fertilization methods used and carrying out land consolidated projects. The organic matter is too high in many counties, and the soil is acidic or alkaline, which shows that farmers depend on cultivated land excessively. Soil acidity or alkalinity can reduce yields by reducing phosphorus (P) availability and increasing aluminum (Al) and manganese (Mn) toxicity [51,52]. The shortage of the labor force reflects the smallholder cropping systems [4] that have mainly been used by the labor force for a long time. It is an important factor affecting the comprehensive quality of CL. We find that private land use decisions depend critically on land quality and have been steered by anticipated economic returns to alternative uses, which in some cases have been affected significantly by public policies, sometimes intentionally and sometimes unintentionally [53]. There is strong evidence that the observed declines in croplands over the past two decades have been caused by falling crop net returns and the existence of the Conservation Reserve Program (CRP), as has been suggested by other research [54]. With the continuous progress of urbanization, rural labor is lost, and the degree of agriculture sideline operations is rising. In the existing land management system, situations such as land fragmentation by road networks and unstable management rights are difficult to change [27]. To protect basic crop yields and profits, the use of chemical fertilizers, pesticides and other chemical products and abandonment of cultivated land will likely increase, the land sustainable utilization will be under threat [27]. Despite this situation, the analysis of limiting factors further affirms the need for integrated sustainable land management (SLM) as a strategy to ensure nutrient availability and improve LUPCs. Efforts to enhance SLM should work on promoting the transfer of regional use rights of CL, advocating scale management and at the same time raising the level of agricultural mechanization. Sustainability 2017, 9, 1312 18 of 21

Table 6. Statistics of the main factors limiting the comprehensive quality of arable land.

Natural Quality Limited Type Organic Irrigation Groundwater Labor per Unit Farmland Involved Counties Metric pH GDP Form Field Matter Guarantee Rate Depth Planted Area Continuity Zhijiang, Qianjiang, Maximum 100 90 100 100 100 60 100 100 Shishou, Jianli, Minimum 50 50 50 50 20 40 70 70 Honghu Mean 79.29 72.86 84.29 78.57 72.86 42.86 93.57 92.86 Use Convenience Limited Type Profile Irrigation Labor per Unit Distance to Water Involved Counties Metric pH Obstacle Depth GDP Configuration Guarantee Rate Planted Area Expressway Resources Area Maximum 100 100 100 100 100 40 100 90 Songzi, Jianli, Minimum 50 90 80 50 60 40 60 40 Honghu Mean 88.33 91.67 95.83 94.17 88.33 40 73.33 69.17 Use Stability Limited Type Per Capita Net Organic Irrigation Labor per Unit Distance to Water Involved Counties Metric pH GDP Income of Matter Guarantee Rate Planted Area Expressway Resources Area Farmers Changyang, Maximum 100 100 100 100 100 100 100 100 Dianjun, Badong, Minimum 50 50 50 20 40 40 80 60 Hefeng Mean 86.07 75.36 74.29 65 71.43 44.29 88.57 71.79 Natural Quality and Use Convenience Limited Type Obstacle Irrigation Labor per Unit of Farmland Distance to Water Involved Counties Metric pH Form Field Depth Guarantee Rate Planted Area Continuity Expressway Resources Area Maximum 100 100 100 40 100 100 80 100 Zhijiang, Dangyang Minimum 70 50 50 40 60 60 60 40 Mean 87.5 62.5 60.94 40 74.06 72.81 66.56 74.69 Natural Quality and Use Stability Limited Type Profile Irrigation Per Capita Net Labor per Unit Involved Counties Metric pH Obstacle Depth GDP Form Field Configuration Guarantee Rate Income of Farmers Planted Area Hanchuan, Maximum 70 100 10 100 100 100 60 100 Qianjiang, Shishou, Minimum 50 50 50 50 60 60 40 60 Jianli, Honghu Mean 60 73.89 78.33 79.44 68.89 78.89 43.33 86.67 Sustainability 2017, 9, 1312 19 of 21

4. Conclusions Evaluating and monitoring land quality is a complex and challenging undertaking [17]. It has become an important activity in China because of the requirement of identify the changes and the natural, socioeconomic attributes of UCL to protect land without specific decisions to perform project investment and land consolidation. This paper establishes a proper comprehensive assessment framework of UCL for the south-central and southwestern portions of Hubei Province in China using the GALQ method. Assessment supplement 12 proposed land use indicators on the basis of the natural factors of GALQ. The weights are determined by the improved model of fuzzy Analytic Hierarchy Process (FAHP). The scores are determined by Natural breakpoint method to standardize the factor values and assigned different grades, which are implemented by ArcGIS. Use the coefficient correction idea, we complement the LUPC into calculating of land use correction index, re-divide the grade of CLQ. The overall level of CLQ in the study area was moderate, slightly higher than the national average. Assessment supplement 12 proposed land use and production indicators and used the land use coefficient correction method to identify the external factors that cause the spatial variability within the study area. The CV is the most commonly used measure of soil variability [45,55]. It found that the spatial differences in the CV in mountainous areas is greater than that in plain areas, and the natural attributes of cultivated land resources are still the main factors that restrict the quality of UCL. In addition, the small-scale agricultural development pattern, which depends strongly on the labor force, was the key extrinsic factor restricting the improvement of regional comprehensive CLQ. The resulting fragmentation of the cultivated land, the disintegration of the farmland road network, and the intensive development of the highway network associated with urbanization are important factors that restrict the convenience and stability of cultivated land use. The National Resource Inventory assessments are conducted at five-year intervals [56], as not all of the soil quality factors vary significantly with land use [56]. Since we have not investigated whether the changes in the LUPCs are significant, two-year intervals are proposed for monitoring UCL. This research provided adequate information on land-quality differences, as well as the spatial variability and characteristics of extrinsic factors among different management systems, as the outcomes are comparable at different locations. This study provides a valid basis for achieving a national balance in the requisition and compensation of cultivated land and perform in gland management of UCL. This study makes a significant contribution towards providing the theoretical and technical references that are needed for the national project of the evaluation and monitoring of UCL. Further study is needed to determine whether using ecological indicators. It can be used to evaluate the comprehensive quality of UCL to achieve the coordination of cultivated land production and ecological functions. We suggest that a decision support [47] from the environmental department to arrive at better informed land management decisions.

Acknowledgments: This work was supported by the National Key National Key R&D Program of China (2016YFC0803106). The authors would like to thank Qingyun Du and Liming Jiao from Wuhan University for their valuable suggestions. We also thank the Department of Land and Resources of Hubei Province and Land Resources Bureau of the counties within the study area for providing the data and materials. Author Contributions: Lina Peng conceived and designed the study with the support of Qingyun Du. All of the co-authors drafted and revised the article together. All of the authors read and approved the final manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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